Date: 16 May 2024, Thursday
\nTime: 14.30
\nPlace: MA -205<\/p>\n
\u201cPredictive Modeling of Return Occurrence in E-Commerce Apparel Market: A Comparative Study of Logistic Regression, LASSO, XGBoost, and Random Forest Techniques\u201d
\nby
\nAsl\u0131 Kutlu
\n(Advisor : Asst. Prof. Yasemin Limon Kahyao\u011flu)<\/p>\n
Abstract:
\nThis study focuses on the development of a predictive model for return occurrence in the apparel segment of an e-commerce company based in Turkey. Leveraging data provided by the company, the research employs various machine learning techniques to explore the impact of various factors on return. Models are developed, incorporating predictor variables related to product, supplier, customer and shopping information with the final model also including interaction of these variables. LASSO regularization is applied to simplify the final model and select the most relevant variables. Performance metrics such as AUC score, accuracy, precision, and recall are evaluated for the models, with comparisons made between logistic regression, LASSO, XGBoost, and Random Forest techniques. Findings indicate that logistic regression models outperform XGBoost and Random Forest in terms of AUC score.<\/p>\n","protected":false},"excerpt":{"rendered":"
Date: 16 May 2024, Thursday Time: 14.30 Place: MA -205 \u201cPredictive Modeling of Return Occurrence in E-Commerce Apparel Market: A Comparative Study of Logistic Regression, LASSO, XGBoost, and Random Forest Techniques\u201d by Asl\u0131 Kutlu (Advisor : Asst. Prof. Yasemin Limon Kahyao\u011flu) Abstract: This study focuses on the development of a [\u2026] <\/p>\n